The impact of assimilating thermodynamic profiles measured with lidars into the Weather Research and Forecasting (WRF)-Noah-Multiparameterization model system on a 2.5-km convection-permitting scale was investigated. We implemented a new forward operator for direct assimilation of the water vapor mixing ratio (WVMR). Data from two lidar systems of the University of Hohenheim were used: the water vapor differential absorption lidar (UHOH WVDIAL) and the temperature rotational Raman lidar (UHOH TRL). Six experiments were conducted with 1-hour assimilation cycles over a 10-hour period by applying a 3DVAR rapid update cycle (RUC): 1) no data assimilation 2) assimilation of conventional observations (control run), 3) lidar−temperature added, 4) lidar−moisture added with relative humidity (RH) operator, 5) same as 4) but with the WVMR operator, 6) both lidar−temperature and moisture profiles assimilated (impact run). The root-mean-square-error (RMSE) of the temperature with respect to the lidar observations was reduced from 1.1 K in the control run to 0.4 K in the lidar−temperature assimilation run. The RMSE of the WVMR with respect to the lidar observations was reduced from 0.87 g kg −1 in the control run to 0.53 g kg −1 in the lidar−moisture assimilation run with the WVMR operator, while no improvement was found with the RH operator; it was reduced further to 0.51 g kg −1 in the impact run. However, the RMSE of the temperature in the impact run did not show further improvement. Compared to independent radiosonde measurements, the temperature assimilation showed a slight improvement of 0.71 K in the RMSE to 0.63 K, while there was no conclusive improvement in the moisture impact. The correlation between the temperature and WVMR variables in the static-background error-covariance matrix affected the improvement in the analysis of both fields simultaneously. In the future, we expect better results with a flow-dependent error covariance matrix. In any case, the initial attempt to develop an exclusive thermodynamic lidar operator gave promising results for assimilating humidity observations directly into the WRF data assimilation system.

Extreme heavy precipitation events (HPEs) pose a threat to human life but remain difficult to predict because of the lack of adequate high frequency and high-resolution water vapor (WV) observations in the low troposphere (below 3 km). To fill this observational gap, we aim at implementing an integrated prediction tool, coupling network measurements of WV profiles, and a numerical weather prediction model to precisely estimate the amount, timing, and location of rainfall associated with HPEs in southern France (struck by ~ 7 HPEs per year on average during the fall). The Water vapor Lidar Network Assimilation (WaLiNeAs) project will deploy a network of 6 autonomous Raman WV lidars around the Western Mediterranean to provide measurements with high vertical resolution and accuracy to be assimilated in the French Application of Research to Operations at Mesoscale (AROME-France) model, using a four-dimensional ensemble-variational approach with 15-min updates. This integrated prediction tool is expected to enhance the model capability for kilometer-scale prediction of HPEs over southern France up to 48 h in advance. The field campaign is scheduled to start early September 2022, to cover the period most propitious to heavy precipitation events in southern France. The Raman WV lidar network will be operated by a consortium of French, German, Italian, and Spanish research groups. This project will lead to recommendations on the lidar data processing for future operational exploitation in numerical weather prediction (NWP) systems.

We discuss the analysis impact of the ensemble-based assimilation of differential absorption lidar observed water vapour and Raman lidar observed temperature profiles into the Weather Research and Forecasting model at convection-permitting scale. The impact of flow-dependent background error covariance in the data assimilation (DA) system that uses the hybrid three-dimensional variational (3DVAR) ensemble transform Kalman filter (ETKF) was compared to 3DVAR DA. The 3DVAR-ETKF experiment resulted in a 50% lower temperature and water vapour RMSE than the 3DVAR experiment when taking the assimilated lidar data as reference and 26% (38%) lower water vapour (temperature) RMSE when comparing against independent radiosonde observations collocated with the lidar site. The planetary boundary-layer height of the analyses compared to independent ceilometer data provided additional evidence of improvement. The 3DVAR analysis RMSE showed 140 m, whereas 3DVAR-ETKF showed 60 m. Although limited to a single case study, we attribute these improvements to the flow-dependent background error covariance matrix in the 3DVAR-ETKF approach. The vertical profile measured from a single stationary lidar system established a spatial impact with a 100 km radius. This seems to indicate future assimilation of water vapour and temperature data from an operational lidar network. The assimilation impact persisted 7 hr into the forecast time compared with the ceilometer data and 4 hr with GPS observations.

<p>Data assimilation (DA) is a tool that is capable of combining observations and numerical weather models (NWMs) in an optimal manner. Current DA systems used by operational forecasting centres are constantly evolving and getting better than before. High-quality observations are very important for the accurate representation of variables in a weather model. In this study, we are incorporating Global Navigation Satellite System (GNSS) tropospheric gradients and Zenith Total Delays (ZTDs) into the Weather Research and Forecasting (WRF) model. WRF model has its operator already developed for the ZTDs and in this research, we are developing a new operator for the assimilation of tropospheric gradients. The assimilation of ZTDs, which are closely related to Integrated Water Vapor (IWV) above the GNSS station, has a positive impact on weather forecasts. On the other hand, tropospheric gradients are not yet assimilated by the weather agencies. Our research is based on a project titled “Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction” (EGMAP) focusing on the impact of GNSS tropospheric gradients and how it can be effectively used for operational forecasting of severe weather. EGMAP is funded by the German Research Foundation (DFG).</p> <p>The observation operator currently in use for tropospheric gradients is based on a linear combination of ray-traced tropospheric delays (Zus et al., 2022). This observation operator is challenging to be implemented into an NWM DA system. We will thus rely on a more simple and fast observation operator which is based on the closed-form expression depending on the north–south and east–west horizontal gradients of atmospheric refractivity (Davis et al., 1993).</p> <p>Initial testing of the operator is done on a 0.1 x 0.1-degree mesh configured over Central Europe in the WRF model with 50 vertical levels up to 50 hPa. The model configuration will be later upgraded to a convective-scale resolution after initial testing of the tropospheric gradient operator. Model forcing observations are derived from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data at 0.25-degree resolution. Conventional observations are obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) and are the base dataset for the assimilation studies. The conventional datasets used for assimilation are restricted to surface stations (SYNOP observations) and radiosondes. Additionally, observations from roughly 100 GNSS stations are assimilated at each DA cycle. Three experiments are conducted: 1) Control run with only conventional data; 2) ZTD assimilation on top of the control run, and; 3) ZTD and tropospheric gradient assimilation on top of the control run. Initial DA tests are being performed with an automated rapid update cycle DA framework with 6 hourly intervals based on a deterministic three-Dimensional Variational (3DVar) DA system for the testing of ZTDs and tropospheric gradients. The DA system will be later upgraded to a probabilistic one based on the Hybrid 3DVar-Ensemble Transform Kalman Filter (-ETKF, Thundathil et al., 2021) and 4DEnVar. The EGMAP project status and initial results from the impact of GNSS-ZTDs and tropospheric gradients will be presented.</p>

<p>Raw data collected at a single Global Navigation Satellite System (GNSS) station allow the estimation of the Zenith Total Delay (ZTD) and the tropospheric gradient. In order to make use of such data in Numerical Weather Prediction (NWP) the observation operators must be developed. Our current observation operator for tropospheric gradients is based on a linear combination of ray-traced tropospheric delays (Zus et al., 2022). Although this observation operator is tuned for high speed and precision it remains difficult to be implemented into NWP Data Assimilation (DA) systems. In this contribution we introduce a simple and fast observation operator which is based on the closed-form expression depending on the north–south and east–west horizontal gradients of radio refractivity (Davis et al., 1993). We run the Weather Research and Forecasting (WRF) model (horizontal resolution of 10km) and find that for the considered geographical region (central Europe) and time period (summer season) the root-mean-square deviation between the tropospheric gradients calculated by the fast and original approach is about 0.15 mm. In essence, the observation operator error is non negligible but acceptable for assimilation. In a first step we implemented the developed operator in our experimental DA system (Zus et al., 2019) and run a series of experiments to check the usefulness of the new approach. We present results from this assimilation experiments where we utilize both simulated and real observations. In the next step we will implement the fast observation operator in the WRF DA system in support of the research project EGMAP (Exploitation of GNSS tropospheric gradients for severe weather Monitoring And Prediction).</p> <p>Davis, J., Elgered, G., Niell, A., and Kuehn, K.: Ground-based measurement of gradients in the “wet” radio refractivity of air, Radio Sci., 28, 1003–1018, 1993. </p> <p>Zus, F.; Douša, J.; Kačmařík, M.; Václavovic, P.; Dick, G.; Wickert, J. Estimating the Impact of Global Navigation Satellite System Horizontal Delay Gradients in Variational Data Assimilation. <em>Remote Sens.</em> 2019, <em>11</em>, 41.</p> <p>Zus, F., Galina, D., and Wickert, J.: Development of a cost efficient observation operator for GNSS tropospheric gradients, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1079</p>